Directory UMM :Data Elmu:jurnal:I:Information and Management:
Information & Management 36 (1999) 185±204
Research
The in¯uence of attitudes on personal computer utilization
among knowledge workers: the case of Saudi Arabia
Muhammad A. Al-Khaldi*, R.S. Olusegun Wallace
Department of Accounting and MIS, College of Industrial Management, King Fahd University of Petroleum and Minerals,
Dhahran 31261, Saudi Arabia
Received 22 April 1998; accepted 24 March 1999
Abstract
Since the introduction of personal computers (PCs) in the early 1980s, Saudi Arabia has made major investments in PCs to
match its rapidly growing economy. As a result, the PC business has become one of the fastest growing sectors in the Kingdom
of Saudi Arabia.
Our paper reports on the results of a study which investigates the relationships between end-users' attitudes and PC utilization among knowledge workers in the context of Saudi Arabia. To gain a better understanding of the factors that in¯uence the
use of PCs, we adopted Triandis' theory which suggests that behavior is determined by attitudes, social norms, habits and
expected consequences of behavior. Our study is based on previous efforts to test the theory's validity in Saudi Arabia.
Our results suggest that PC utilization is determined by individual attitudes, personal characteristics, such as PC experience,
facilitating conditions, such as PC access and social factors. We also observed that respondents to our questionnaire differ in
the level of importance they attribute to the factors hypothesized as in¯uencing PC utilization compared to Canadian
respondents in a previous study. # 1999 Elsevier Science B.V. All rights reserved.
Keywords: PC utilization; User attitudes; Social factors; Knowledge workers; Saudi Arabia
1. Introduction
One aim of our study is to ascertain the attitudes of
knowledge workers in Saudi Arabia [34] toward the
use of PCs, and to determine whether those attitudes
are similar to those reported by Thompson et al. [41,
42] for knowledge workers in Canada. There are
reasons for replicating the Canadian study in the
Kingdom of Saudi Arabia. Essentially, knowledge
about the factors that promote the usage of PCs is
useful to the global economyÐfar too important to be
*Corresponding author. Tel.: +966-3-860-2656; fax: +966-3860-3489; e-mail: makhaldi@kfupm.edu.sa
limited to the ®ndings from one national study. Different contexts allow us to understand variation across
the world. The Canadian context is different from the
Saudi context both from the point of view of economic
development and cultural orientation. First, Canada is
more developed than Saudi Arabia. In addition, Saudi
Arabia has only recently started to develop the educational, organizational and institutional systems that
have long existed in developed countries, such as
Canada. Second, Canada is essentially a secular community, while Saudi Arabia is an Islamic society with
a culture that manifests high power distance, uncertainty avoidant, collectivist, and femininity characteristics (along Hofstede's [23] cultural dimensions (see
0378-7206/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved.
PII: S - 0 3 7 8 - 7 2 0 6 ( 9 9 ) 0 0 0 1 7 - 8
186
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
[4, 5, 12]). Although the potential importance of PCs
in the social and economic development of Saudi
Arabia is recognized by Saudi authorities [11], the
need to uphold the tenets of Islam was probably
responsible for the delay (until January 1999) in
allowing public access through the PC to the enormous
potential of the internet within Saudi Arabia. Given
these societal differences, we would expect the PC
utilization attitudes of the respondents in Saudi Arabia
to differ from those of Canadian respondents. Another
aim of the study is to explore the importance of factors
(such as attitudesÐaffect, beliefs, behavior; societal
and facilitating conditions) in promoting the usage of
PCs in Saudi Arabia.
The Kingdom of Saudi Arabia has a population of
12.3 million citizens and 4.6 million foreign nationals,
mainly from the Indian sub-continent and East Africa.
More than 50 percent of the Saudi population are
under the age of 16. The country has a stable economy
and is a conducive environment for commerce and
business. Its commercial sector is relatively unregulated and the ®nancial sector is liberalized. Saudi
Arabia's microcomputer and minicomputer market
exhibits growth rates similar to the American market
[2]. As a result, it would be interesting to learn whether
the relative strength of attitudes towards PC usage in
Canada holds in Saudi Arabia. It is also interesting
because, in contrast to previous studies of the factors
capable of in¯uencing PC utilization in the Kingdom
(e.g., [1, 9, 10]), our study used practitioners rather
students as subjects of enquiry.
2. Conceptual framework and research questions
The link between end-user attitudes and PC utilization has occupied the attention of scholars in recent
years. The different frameworks include:
1. The theory of reasoned action (proposed by
Fishbein and Ajzen [18] and adopted by Thomp-
2.
3.
4.
5.
6.
son et al.) which seeks to investigate the reasoning
that lies behind the decision to utilize PCs. This
fact and the theory of perceived behavioral control
[6, 7, 8] suggest that PC usage can be predicted by
an individual's intention to use it. This intention is
determined by some weighted combination of the
individual's attitudes toward PC-related objects
(hardware, software, etc.).;
The theory of perceived behavioral control, based
on the individual's intention to perform a given
task, is an extension of the theory of reasoned
action to take account of behaviors over which
people have incomplete control;
Technology acceptance model proposed and
adopted by Davis et al. [14]. This model highlights
perceived ease of use and perceived usefulness of
the PC;
Expectancy model (proposed by Vroom [45] and
adopted by DeSanctis [16]) based on the belief that
the use of PCs leads to good performance, which in
turn leads to desired outcomes or the valence of
available outcomes;
Job diagnostic survey (utilized by Yaverbaum
[47]); and
Computer attitude scale (developed by Loyd and
Gressard [29] and used in experiments [20, 21, 30,
31] by them, Al-Jabri [3] and Al-Khaldi and AlJabri [9]). This framework examines four different
factorsÐanxiety or fear of PCs, con®dence in
ability to use or learn them, liking them, and their
usefulness.
Our study draws upon all these. Attitudes are determined by the individual's beliefs about the consequences of his or her behavior, based on the social
norms and mores. The relationship is illustrated in
Fig. 1.
In this context, user attitude is construed as a
learned predisposition toward PC-related objects.
Once the concept is de®ned thus, its operationalization
requires the identi®cation of all PC-related objects
Fig. 1. End-user attitude as a determinant of PC usage.
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
affecting end-user's attitudes and the development of a
measurement instrument to gauge the user's reaction.
While Fishbein and Ajzen considered all beliefs about
an act or behavior, Triandis [43, 44], in a modi®cation
of the Fishbein and Ajzen's theory, makes a clear
distinction between beliefs that link emotion to the act
(occurring at the moment of action) and beliefs that
link the act to future consequences. Triandis argues
that an individual's intended behavior is determined
by his or her feelings toward the object (Affect),
expected consequences of the behavior (cognition)
and what she/he should do (social factors).
The desire to understand end-users' behaviorÐ
their attitudes and perceptions toward the availability
of the PC, its costs and its ease of useÐcompels the
adoption of a multi-disciplinary approach. This
requires the use of a cumulative research tradition
by which theories and models borrowed from other
disciplines serve as a foundation [19, 27, 38]. For
example, a cognitive account of PC utilization is a
theory about its functional capacitiesÐthe things it
can doÐthat are involved during the completion of the
questionnaire. There are numerous ways of describing
the range and kinds of functional capacities involved
in any aspect of PC utilization but most scholars have
found it useful to describe these capacities in terms of
attitudes. However, probably because of the minimal
use of the knowledge accumulated in the social psychology literature, the empirical evidence on whether
PC utilization is in¯uenced by end-users' attitudes is
mixed and inconclusive; (e.g., see [40]). In addition,
little evidence relates to developing countries in general, and the Arabian Gulf Region in particular. To
facilitate our understanding of attitudinal factors of PC
utilization, we proposed the `working model' depicted
in Fig. 2.
The proposed model is a simpli®cation, as we are
not attempting to cover all relevant aspects of PC
utilization. The model groups many of the individual/
organizational motives for PC utilization into four
parts: (1) individual attitudes toward the PC; (2) social
factors capable of affecting PC usage; (3) individual
pro®les (age, experience, education, access and training of the respondents); and (4) organizational factors
which facilitate its use. The motives that are described
in the model as attitudes are disaggregated for analytical convenience into three portions, along the lines
suggested by Fishbein and Ajzen: affect, behavior, and
187
cognition. A theory of PC utilization would not be a
complete theory of attitude if the emotional and
intentional dimensions are excluded, thus previous
studies have examined the role of the affective and
cognitive faculties. In our study, these are referred to
as affect and behavior, respectively.
We separated the cognition motive into short-term
and long-term consequences. The ®rst has two dimensionsÐcomplexity (anxiety or fear of the PC) and
usefulness (how it ®ts the tasks and job of the enduser). Speci®cally, the study addresses two sets of
questions:
1. What are the factors that have the highest
in¯uence on the end-users' utilization of PCs in
Saudi Arabia? and what is the comparative
importance of attitudinal factors in different
countries? For example, do end-users in different
countries have similar attitudes? Do countryspeci®c conditions imply different PC usage?
2. Are there within-country differences in the importance of the attitudinal factors? For example, Culpan [13] has shown that there are perceptual
differences in the importance of attitudinal factors
across industries in south-central Pennsylvania. On
this basis, we investigate whether our respondents
differ in their perceptions of the degree of importance of the factors which may in¯uence PC utilization. We also investigate whether any
differences in the respondents' perceptions are
due to the differences in their pro®les.
3. Operationalization of constructs
To operationalize the constructs we adopted and
amended the instrument designed by Thompson et al.
The constructs are:
(1) Social factors. This set of factors deals with
within-organization socialization on the use of PCs
and comprises four items: (1) support of senior management of the department introducing PCs; (2) the
CEOs and managers' support of the use of PCs for
jobs; (3) the general support of the organization in the
introduction of PCs; and (4) the proportion of departmental workers who use PCs. These factors are presumed to capture the signals and messages that an enduser receives from his/her peers and others about what
188
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Fig. 2. Factors in¯uencing PC utilization.
they should do. They deal with an individual's internalization of work ethics and mores in organizations
and capture what individuals perceive as an acceptable
mode of conduct, given their generally accepted organizational culture. In line with Thompson et al., we
hypothesize that:
H1: There is a positive relationship between social
factors concerning the use of PCs and PC utilization.
Each of the four factors was measured by a simple
questionnaire item. For the ®rst three, the items were
scaled by a ®ve-point Likert-type scale (1 strongly
disagree to 5 strongly agree). The fourth item was
scaled using a ®ve-point scale representing different
proportions (1 10% or less to 5 90% or more).
(2) Affect factors represent respondents' affection
and disaffection for the PC. While we recognize
that affective and cognitive components of attitude
may overlap, we have adopted the strategy of separating the two as suggested by Thompson et al.
Affection for the PC is a function of an individual's
previous training and awareness of the PC. We
hypothesize that:
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
189
H2: There is a positive relationship between affect
and PC utilization.
H4: There is a positive relationship between perceived job ®t and PC utilization.
Affect relates to three items: (1) PCs make work
more interesting; (2) working with PCs is fun; and (3)
PCs are good for some tasks but not for the kind of task
I want. A ®ve-point Likert-type scale was used
(1 strongly disagree to 5 strongly agree).
(3) Cognitive factors. This set of factors deals
with the perceived consequences of adopting a PC
for the individual, such as `working with the PC is
complicated or it takes too long to learn how to use a
PC to make it worth the effort.' As Thompson et al.
suggest, the perceived consequences construct is
consistent with the expectancy theory of motivation, proposed by Vroom, which suggests that individuals evaluate the consequences of their behavior
in terms of potential rewards and base their actions
on the attractiveness of the rewards. Perceived
consequences may be positive or negative, just as
they can be high or low. The consequences may be
felt immediately on the use of a PC or may produce
a valence whose effect may extend beyond the shortterm to the long-term. We examine two perceived
short-term consequences and one perceived long-term
consequence.
3.2. Long-term consequences
3.1. Short-term consequences
(a) Complexity. Because complexity refers to the
dif®culty that an individual may experience while
using the PC, we hypothesize that:
H3: There is a negative relationship between perceived complexity and PC utilization.
Four complexity items were given to the respondents and a ®ve-point Likert-type scale was used to
capture their opinions on each (1 strongly disagree
to 5 strongly agree).
(b) Usefulness or job performance facilitation
(JPF). This set of perceived behavioral control factors
was captured, using six items that describe the potential outcomes and valences from using a PC; e.g., the
use of a PC can decrease the time needed for my
important job responsibilities. These refer to the PCs
potential for enhancing the individual's job performance. We hypothesize that:
The positive and negative effects of PC utilization
can often be felt on work performed long after the PC
was introduced. Such factors were captured with six
items, using a ®ve-point Likert-type scale. Respondents were asked for their opinion on the perceived
understanding of the long-term effect of using the PC.
We hypothesize that:
H5: There is a positive relationship between perceived long-term consequences of PC use and PC
utilization.
The six types of effect on job characteristics are: the
level of challenge of the respondents' job; the opportunity for preferred future jobs; the amount of variety
on the job; the opportunity for more meaningful work
in the future; the ¯exibility of changing jobs in the
future; and the opportunity for job security. The effects
that the respondents were asked to consider include
the potential for increase, decrease, or no change in
each of the six job characteristics.
(4) Facilitating factors refer to the availability of
support systems. Six items were also used to operationalize these factors. We adopt Triandis's de®nition
of facilitating factors as the ``objective factors, `out
there' in the environment, that several judges or
observers can agree make an act easy to do.'' We
assumed that there was a positive association between
supporting systems and PC utilization. We did not
factor into our study conditions which impede PC
utilization. As a result, we hypothesize that:
H6: There is a positive relationship between supporting facilitating conditions and PC utilization.
(5) PC utilization factors were captured using three
dimensions: the intensity of PC use; frequency of PC
use and diversity of software packages used. Intensity
was measured by the number of minutes devoted to the
use of PC per day at work. Frequency of use was
measured by four categories, ranging from less than
once per week to several times a day. The diversity of
use was measured by counting the number of software
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M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
packages optionally used at work, which ranging from
1 to 5.
(6) Other factors investigated include PC training,
PC experience, and age. Training was indicated by
Thompson et al. as an element that needs to be
investigated with regard to PC attitude and it was
later investigated by them. Respondents that are
trained in the use of the PC would most probably
tend to attribute greater importance to PC utilization
motivators than those who are not. The more exposed
to PC training, the more likely that there would be
perceived positive long-term consequences. As a
result, we hypothesize that:
H7: There is a positive relationship between PC
training and PC utilization.
Other studies have found a signi®cant effect of PC
access and experience on reducing PC anxiety and
enhancing its utilization. We predict PC utilization to
increase as the individual respondent gains more
access to the PC, or as he gains more experience
working with the PC. PC familiarity and experience
can reduce fears and negative attitudes toward the PC
and can encourage PC utilization. We hypothesize
that:
H8: There is a positive relationship between the
degree of access to the PC and PC utilization.
H9: There is a positive relationship between the
extend of PC experience and PC utilization.
Given the relatively nascent usage of PCs in the
Kingdom of Saudi Arabia, it is possible that the level
of education and age may be major factors in the
utilization of the PC. We predict that the level of
education would be positively associated with PC
utilization and that age would be negatively related
to PC utilization. Younger employees are likely to
have had some training in the use of PCs in their
education, while older people would have gone to
school at a time when PC education was not available.
These speculations form the bases of following
hypotheses:
H10: There is a positive relationship between education level and PC utilization.
H11: There is a negative relationship between age
and PC utilization.
3.3. Organization size
Organization size has been suggested as a predictor
of PC adoption in organizations [15, 17, 25, 35, 36].
On the basis of empirical research on ®rms, we suggest
that organization size may be equally signi®cant
for PC utilization and that it may have a stronger
moderating in¯uence than individual attitudes on
PC utilization by knowledge workers. The larger a
®rm is, the greater are the prospects of computerization. If this is true, this challenges one of the most
central orthodoxies of PC utilization studies, and
implies that those seeking to promote PC utilization
ought, in choosing the method of motivating PC
utilization, to give as much, or more weight to
organization size, as to other motivating factors.
Firms with substantial size can attain more effective
transition from manual to PC systems through investment in training. Growth in size generates pressures
for computerization.
According to Raymond [37], smaller ®rms can
hardly afford the enormous costs of PC utilization
(employment of persons with specialized knowledge,
training staff, transformation of manual to PC systems,
acquisition). The activities of small ®rms are often
below the level that is optimal for computerization.
While previous studies have examined the factors that
in¯uence PC utilization in small ®rms within a country
(e.g., New Zealand, Saudi Arabia and Taiwan [24]),
the studies have often assumed that the factors that
promote PC utilization in small ®rms would be different from those in large ®rms. This assumption is
based on intuitive and reasoned conclusion rather than
empirical investigation. We use number of employees
to measure organization size. This has been used as a
measure of organization size in several studies in
accounting (e.g., [46]).
4. Methodology
4.1. Sample
The study was conducted in the Eastern Province of
Saudi Arabia. The survey locality is typical of the
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
country. It is the headquarters of the dominant ®rms in
the Kingdom and has a great number of organizations
(light industries, retail outlets and many government
administrative of®ces). Most of the respondents work
in semi-government pro®t-seeking ®rms and private
®rms located in Dhahran and Dammam, the cities with
the largest concentration of knowledge workers in the
Eastern Province. A sample of 200 knowledge workers was targeted. Knowledge workers whose opinions
are pooled for this study include accountants, treasurers and controllers, general managers, engineers,
production analysts, and corporate planners. They
generally use low-end applications software and not
decision support or expert systems. However, the
knowledge workers who participated in the study as
end-users come within the ®rst three categories of the
six end-user categories identi®ed by Rockart and
Flannery [39]:
1. non-programming end-users, who access data
through predeveloped menu-driven software
packages;
2. command level end-users, who generate unique
reports for their own purposes, usually with simple
query languages; and
3. end-user programmers, who utilize command and
procedural languages to access, manipulate, and
process data for their personal information needs.
In total, after one follow-up (and telephone communication) with the contact persons in each participating
®rm, 151 (75.5%) useful responses to the questionnaire were collected from the participants. Every
questionnaire which achieves less than 100 percent
response rate has a potential non-response bias problem. Like most researchers, we have addressed this
problem in two ways.
1. By attempting to obtain as high a response rate as
possible. However, questionnaires dealing with
sensitive personal issues may be more likely to
result in non-response. Users who are not
comfortable with the PC may be reluctant to
respond, while competent users may be particularly interested in the subject and willing to
participate in the study. Here, we attempted to
reduce non-response bias by using a follow-up
procedure and the coordinator within a participating ®rm to chase potential non-respondents.
191
2. By estimating qualitatively the potential effect of
non-response on the ®ndings. On this basis, we
conducted a comparative analysis of responses by
date of response (or date of receipt of responses).
This analysis is based on the presumption that late
responders are reasonable `surrogates' of nonrespondents. The response rate of 75.5% is the
average of responses from the entire sample; the
response rates from participating ®rms vary from a
low of 60 percent to a high of 100 percent. Every
effort was made to minimize the occurrence of nonresponse within a returned questionnaire. Where
one was noticed, the individual ®rm from where the
incomplete questionnaire came was consulted and
the contact person in that ®rm was asked to trace
the individual respondent to get the required
response.
The questionnaire used to elicit opinions from respondents is a slightly modi®ed version of an instrument
used by Thompson, et al. for Canadian surveys. The
present study differs from these studies in the following ways. First, it differs in terms of the spread of
respondents. Whereas the Canadian study collected
responses from participants drawn from a large multinational manufacturing ®rm, the respondents to our
study were drawn from 10 of the leading ®rms in the
Eastern Province of Saudi Arabia. Second, the questionnaires were not completed, using the DISKQ
technique adopted by Thompson et al. Instead, we
employed a paper and pencil technique. We concluded
that an interactive questionnaire was not suitable for
the present environment of Saudi Arabia. The use of
the method would affect the response rate in an
environment where people are not used to it.
5. Procedure
In each participating ®rm (see Table 1 for the
characteristics of the participating ®rms), a coordinator was appointed to arrange the distribution of questionnaires to (and collection of completed ones from)
knowledge workers. The survey envelope included a
cover letter from the researchers and from the coordinator in the participating ®rm, introducing the purpose
of the study and guaranteeing con®dentiality of individual responses. It also indicated that the results
192
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 1
Characteristics of respondent companies
Number of
employees
Type of
firm
Under 50
51±200
Over 200
Small
Medium
Large
Industry classification
1. Manufacturing
2. Construction
3. Agriculture
4. Services
5. Oil and energy
6. Trade, retailing, leasing
No. of
firms
100
24
26
(%)
66.67
16.00
17.33
100.00
40.6
4.2
0.7
25.2
18.9
10.4
100.0
would not refer to individual responses but would be
published in summary form only.
6. Aggregation of responses
The instrument is structured in a way that asked the
respondents (see Table 2 for their pro®le) to indicate
the level of their agreement with perceptual anchors
on the level of importance of each of the suggested
factors capable of promoting PC utilization. The
survey instrument used a Likert scale (varying from
`strongly agree' 5 to `strongly disagree' 1, with
room for equivocation in the middle, i.e.,
`neutral' 3). While factors are not independent,
respondents were asked to treat each factor as independent of the others and to assign as many `5s', `4s',
`3s' etc., as they felt were warranted by the factors
included in the survey instrument; i.e., not to rank
them. The responses to the items within each group
were aggregated to produce indexes for each grouped
variable. For example, the PC utilization index was
derived by averaging the means of the responses to
each of the three items that make up PC utilization.
The validity of the survey instrument was assessed
by a factor analysis. Table 3 contains rotated factor
matrix (varimax) with eight speci®ed factor loadings
on their own constructs than on others. This is the case
with rotated structure. However, there is one exception. Item JPF1 fails to load highly on Factor 2, which
represents the construct job performance facilitation
but loads highly on Factor 8, which captures the affect
items. As a result, the rotated structure appears to be
Table 2
Profile of respondent individuals
Age
No.
%
20±25 years
26±30 years
31±40 years
41±50 years
51 and above
Total
10
29
73
33
5
150
6.7
19.3
48.7
22.0
3.3
100.0
Access to a PC
No access
Low
Average
High
Total
0
12
65
74
151
0.0
7.9
43.1
49.0
100.0
Experience in using the PC
None
Low
Moderate
High
Total
2
31
88
29
150
0.13
20.67
58.67
19.33
100.00
No.
%
Ownership of a PC
Yes
No
Total
79
71
150
52.7
47.3
100.0
Educational background
Less than High school
High school
Some years in college
Bachelor's degree
Graduate degree
Total
1
10
15
85
39
150
0.06
6.67
10.00
56.67
26.00
100.00
193
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 3
Rotated factor matrix of attitude items
Attitude items
1
2
3
4
5
6
7
8
LT1
LT2
LT3
LT4
LT5
LT6
JPF1
JPF2
JPF3
JPF4
JPF5
JPF6
FC1
FC2
FC3
FC4
SF1
SF2
SF3
SF4
COM1
COM2
COM3
COM4
TR1
TR2
UT1
UT2
UT3
AF1
AF2
AF3
0.648
0.784
0.758
0.690
0.655
0.675
0.125
ÿ0.041
0.158
0.191
0.204
0.243
0.215
ÿ0.015
0.052
ÿ0.020
ÿ0.122
0.239
0.076
0.230
0.118
ÿ0.078
ÿ0.127
ÿ0.069
0.158
0.190
0.012
ÿ0.004
0.109
0.225
0.336
0.241
0.148
0.041
0.233
0.122
0.111
0.154
0.172
0.431
0.818
0.787
0.664
0.753
0.026
0.039
0.092
ÿ0.040
0.091
0.218
ÿ0.028
0.075
ÿ0.232
ÿ0.030
ÿ0.021
ÿ0.070
0.046
0.024
0.203
0.053
0.014
0.160
0.198
0.046
0.027
ÿ0.004
0.055
ÿ0.032
0.185
0.066
0.170
ÿ0.049
ÿ0.046
0.113
0.075
0.037
0.646
0.761
0.744
0.785
ÿ0.014
0.292
0.093
0.381
ÿ0.118
0.035
ÿ0.093
ÿ0.279
0.248
0.270
0.141
0.097
0.088
ÿ0.014
0.228
ÿ0.098
ÿ0.055
0.143
ÿ0.030
0.235
0.131
0.124
0.375
0.038
0.087
0.092
0.030
0.056
0.168
0.064
0.140
0.132
0.521
0.695
0.787
0.677
0.068
ÿ0.133
ÿ0.067
0.138
0.010
0.018
0.190
0.041
0.020
0.364
0.029
ÿ0.013
0.054
ÿ0.100
0.015
ÿ0.183
ÿ0.077
0.079
ÿ0.193
0.323
ÿ0.066
ÿ0.056
ÿ0.043
ÿ0.131
ÿ0.010
ÿ0.141
0.046
ÿ0.102
ÿ0.099
0.033
ÿ0.045
ÿ0.113
0.565
0.720
0.775
0.537
0.021
ÿ0.042
0.033
ÿ0.005
0.089
0.182
0.174
ÿ0.225
0.018
0.108
ÿ0.145
0.212
0.079
0.195
ÿ0.203
0.105
0.100
0.047
ÿ0.166
0.007
0.240
0.214
0.100
ÿ0.000
0.024
0.022
0.069
ÿ0.056
ÿ0.179
0.075
ÿ0.158
0.276
0.849
0.838
ÿ0.108
0.036
0.183
0.046
ÿ0.286
ÿ0.161
ÿ0.064
0.089
0.098
ÿ0.074
ÿ0.041
0.053
ÿ0.027
ÿ0.142
0.895
0.176
0.100
0.062
0.006
0.176
0.175
0.005
0.459
0.139
0.146
ÿ0.109
0.136
0.013
0.050
ÿ0.408
0.007
0.000
0.737
0.681
0.494
ÿ0.015
ÿ0.221
0.240
0.121
0.145
0.026
0.116
0.279
ÿ0.145
0.504
0.095
ÿ0.000
0.017
0.052
0.223
ÿ0.070
0.034
ÿ0.060
0.175
ÿ0.006
ÿ0.051
0.177
0.123
ÿ0.466
ÿ0.008
0.026
ÿ0.155
ÿ0.064
ÿ0.014
0.111
ÿ0.081
0.425
0.570
0.374
0.599
consistent with the underlying theoretical constructs
of the survey instrument. In further statistical analysis
of the job performance facilitation items, JPF1 was not
included. For example, the Cronbach's reported for
the job performance facilitation does not include JPF1.
The internal consistency of the eight scales was
assessed using Cronbach's alpha ( ). We report two
inter-rater correlation coef®cients (ICCs) that are
estimates of reliability and validity. ICC(1) is an
estimate of the degree to which responses on each
item are similar, is reported in Table 4, and ICC(2) is
an estimate of the reliability of the mean scores [22], is
reported in the last column of Table 8. ICC(1) and
ICC(2) address two different issues [33]. If two
respondents were randomly sampled from the same
group and their two sets of scores correlate, the
resulting correlations would approximately equal
ICC(2) which is scaled as zero (0) when the observable
level of agreement is minimal and one (1) when there
is perfect agreement. For intermediate values, Landis
and Koch [28] suggest the following interpretations:
below 0.0 poor agreement; 0.00±0.20 slight
agreement; 0.21±0.40 fair agreement; 0.41±
0.60 moderate agreement; 0.61±0.80 substantial
agreement; and 0.81±1.00 almost perfect agreement. ICC(2) ratings of 0.60 and above suggest that
one can reject the hypothesis that respondents were
scoring an item randomly and that one can conclude
that acceptable levels of mean score reliability exist
[32]. ICC(1) indicates the extent to which respondents
within the same group assign the same psychological
meaning to, or agree in their perceptions of, the
194
Alpha
Intercorrelations of social factors items
SF1
SF2
SF3
SF1
SF2
SF3
SF4
1.000
0.286b
0.292b
0.2131a
Intercorrelations of affect
AF1
AF1
1.000
AF2
0.342b
AF3
0.2550a
1.000
0.586b
0.5293b
1.000
0.491b
factors
AF2
AF3
1.000
0.166a
Mean
Standard
deviation
Mean rank
SF4
Mean
1.000
1.000
Intercorrelations of near-term consequences: complexity
CO1
CO2
CO3
CO4
CO1
1.000
CO2
0.286b
1.000
0.420b
1.000
CO3
0.389b
CO4
0.273b
0.309b
0.351b
1.000
Intercorrelations of near-term consequences: job fit (usefulness)
UF1
UF2
UF3
UF4
UF5
JPF1
1.000
JPF2
0.021
1.000
JPF3
0.099
0.160
1.000
0.669
JPF4
0.196
0.140
0.705b
1.000
JPF5
0.196
0.143
0.483b
0.530b
1.000
b
b
JPF6
0.319
0.151
0.655
0.655b
0.532b
Canada
Standard
Deviation
Rank
0.776
0.599
0.610
0.656
3.68
4.01
4.47
4.40
1.22
1.19
0.81
0.84
21st
13th
4th
5th
1.88
3.04
3.81
4.24
0.86
1.21
1.30
1.05
28th
25th
17th
4th
0.285
0.406
0.510
4.58
3.81
3.76
0.62
1.12
1.21
2nd
18th
20th
4.11
4.22
n/a
0.78
0.78
n/a
10th
5th
n/a
0.628
0.605
0.549
0.633
2.46
1.70
2.23
2.15
1.35
0.95
1.67
1.23
28th
31st
29th
30th
4.39
4.47
3.49
4.30
0.94
0.89
1.14
1.07
2nd
1st
19th
3rd
0.780
0.795
4.65
0.656
0.692
0.646
4.18
3.80
0.70
4.51
4.23
4.40
1.23
1.36
1st
0.83
1.05
0.83
8th
19th
4.16
3rd
7th
5th
4.11
3.92
1.02
4.19
4.13
4.07
1.17
1.27
7th
0.97
1.00
0.87
9th
14th
UF6
1.000
6th
8th
11th
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 4
Intercorrelations, means and mean rankings of the importance of attitude items in Saudi Arabia compared with Canadian rankings from the [14] study (See Table 5 for description
of variables)
Intercorrelations of long-term consequences
LT1
LT2
LT3
LT1
1.000
LT2
0.465b
1.000
LT3
0.488b
0.537b
1.000
LT4
0.409b
0.557b
0.391b
b
b
0.550
0.440b
LT5
0.398
LT6
0.2774b
0.5690b
0.4033b
LT4
LT5
LT6
3.88
4.12
4.03
4.16
4.11
3.87
1.06
0.81
0.96
0.84
0.85
1.01
15th
10th
12th
9th
11th
16th
3.39
3.20
2.74
3.23
3.01
3.22
1.17
1.25
1.32
1.23
1.19
1.262
20th
23rd
27th
22nd
26th
4th
1.000
0.786
0.715
0.747
0.731
3.56
3.86
3.68
3.91
1.16
1.02
1.16
1.11
23rd
17th
21st
14th
3.59
3.88
3.57
4.00
1.30
1.25
1.25
1.12
17th
15th
18th
12th
Intercorrelations of training and experience factors
TR1
TR2
EX1
EX2
TR1
1.000
TR2
0.844b
1.000
EX1
ÿ0.046
ÿ0.034
1.000
EX2
0.079
0.084
ÿ0.016
1.000
0.056
0.050
0.603
0.506
3.32
3.41
2.96
7.29
1.43
1.45
0.67
4.87
25th
24th
26th
n/a
n/a
n/a
3.99
3.22
n/a
n/a
1.39
1.08
n/a
n/a
13th
24th
0.350
0.449
0.627
4.20
4.77
3.63
1.04
0.54
1.31
3.56
4.33
3.57
1.38
1.03
1.27
Intercorrelations of facilitating conditions
FC1
FC2
FC3
FC1
1.000
FC2
0.421b
1.000
0.576b
1.000
FC3
0.430b
b
FC4
0.462
0.605b
0.474b
Intercorrelations of utilization
UT1
UT2
UT1
1.000
UT2
0.456b
1.000
UT3
0.289b
0.212a
a
b
p 0.05.
p 0.01.
1.000
0.605b
0.5567b
1.000
0.4279b
1.0000
FC4
0.506
UT3
1.000
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
0.835
0.797
0.823
0.807
0.813
0.824
195
196
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
relevance of research results on a computer utilization
factor. ICC(1) compares the between sample sum of
squares to the total sum of squares from the results of a
one-way analysis of variance. In past research, ICC(1)
values have ranged from 0 to 0.5, with a median of
0.12 [26]. There are no de®nite guidelines on acceptable ICC(1) values.
The responses on each factor were averaged (aggregated) and these were used to determine the relative
importance of each factor. This aggregation of
responses suggests that respondents were agreed on
mean scores though the reported standard deviations
challenge the assumption of convergence. In fact, the
mean scores do not reveal variability across situations,
such as age of respondents or size of the ®rm in which
a respondent is employed; nor do they permit prediction of speci®c behaviors in given situations. To unveil
the fac,ade of convergence from the pooled data, we
analyzed the responses in two different ways. First, we
used the responses showing agreement with the suggestion that a factor is important and those not agreeing with the suggestion of importance to derive an
index of agreement that was introduced by Herbert
and Wallace [22] in their study of the perceptions of
U.K. Chief Finance Of®cers on corporate ®nance
research topics. An index of agreement is the ratio
of total agreeing responses (i.e., 5s and 4s for positive
questions or 1s and 2s for negative questions) to total
disagreeing response (i.e., 1s and 2s for positive
questions and 5s and 4s for negative questions).
The index excludes equivocal (i.e., `not sure' 3)
responses which vary from item to item. To make an
index of agreement comparable in this study, each
index was adjusted to re¯ect the ratio of unequivocal
responses to all responses by multiplying that ratio
with the index of agreement.
Second, we disaggregated the responses on the basis
of organization size into large, medium, and small
®rms. Although the pooled data suggests that many of
the factors warranted more research, signi®cant differences may exist among ®rms of different sizes.
7. Results
The ®rst question is the comparative attitudes of
Canadian and Saudi respondents toward the utilization
of the PC. Since the questionnaire for Saudi respon-
dents was similar to that for the Canadian respondents,
we compared the mean responses from each. The
settings, situations and contexts are our referential
scopes because it is dif®cult to delve into the minds
of our subjects and to substantiate what seems to be
within the realm of mere conjecture. The process of
our reference must, therefore, rely heavily on perceived behavior and we have been concerned exclusively with organizationally constructed modes of
behavior. The results of the comparison of the mean
responses from the Saudi and Canadian respondents
are reported in Table 4.
While the three highest levels of Saudi respondents'
agreement with our suggestible questions were,
respectively, in respect of (1) the ®tness of the PC
to respondents' job (Question JPF3 with mean
response of 4.65, 0.67), (2) affect (AF1, mean
response of 4.58, 0.29) and (3) job ®tness (JPF4,
mean response of 4.51, 0.66), those of the Canadian respondents were mainly with the suggestible
questions on complexity: (1) Question CO2 with mean
response of 4.47, (2) Question CO1, mean response of
4.39, and (3) CO4, mean response of 4.30. It is
surprising that Saudis perceived the PC as relatively
less dif®cult to understand than Canadians. If this
observation is true, we would expect to ®nd that the
level of PC utilization in the two studies would show
that the Canadians would reveal a lower PC utilization
than the Saudis. The results con®rm this expectation
because on each of the PC utilization constructs, the
mean responses from the Saudis were greater than
those from the Canadians.
The results from the use of a different procedure for
aggregating the responses from the Saudi respondents
are similar to those from the use of mean responses,
except that affect item AF1 and job performance
facilitation item JPF3 swapped positions, with job
performance facilitation item JPF6 tying with job
performance facilitation item JPF4 as the items with
the third highest level of respondent agreement with
the suggestible questions.
To test the robustness of our ®ndings on the attitude
items which are accorded highest level of importance
on the basis of mean responses, we developed an index
of agreement along the lines suggested in earlier
discussion. Table 5 reports these indexes of agreement. The ranking of attitude items on the basis of
indexes of agreement is similar to the one from mean
197
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 5
Pooled sample indexes of agreement on the level of importance accorded attitude items (n 151)
Attitude
items
Index of
agreement
Rank
Adjusted index
of agreement
Rank
Social
SF1
SF2
SF3
SF4
The proportion of departmental co-workers who use a PC
The senior management of this business unit has been helpful in introducing PCs
My boss is very supportive of PC use for my job
In general, the organization has supported the introduction of PCs
2.94
5.90
22.67
16.50
20th
9th
5th
6th
2.39
5.43
21.32
15.30
20th
9th
5th
6th
Affect
AF1
AF2
AF3
PCs make work more interesting
Working with a PC is a fun
PCs are okay for some tasks but not the kind of task I want (score reversed)
142.00
4.82
3.38
1st
14th
16th
135.37
4.08
2.88
1st
14th
17th
0.54
0.09
0.30
24th
27th
26th
0.47
0.08
0.25
24th
27th
26th
0.30
5.00
3.35
48.33
23.17
9.62
23.17
25th
13th
17th
2nd
3rd
7th
3rd
0.28
4.80
3.02
47.69
22.39
8.91
22.25
25th
12th
16th
2nd
3rd
7th
4th
Long-term consequences
LT1
Use of a PC will increase the level of challenge on my job
LT2
Use of a PC will increase the opportunity for preferred future job assignments
LT3
Use of a PC will increase the amount of variety on my job
LT4
Use of a PC will increase the opportunity for more meaningful work
LT5
Use of a PC will increase the flexibility of changing jobs
LT6
Use of a PC will increase the opportunity to gain job security
5.71
8.39
5.63
3.04
3.07
0.77
10th
8th
11th
19th
18th
23rd
4.61
7.49
5.22
2.63
2.57
0.52
13th
8th
10th
18th
19th
23rd
Facilitating conditions
FC1
Guidance is available to me in the selection of hardware and software
FC2
A specific person (or group) is available for assistance with software difficulties
FC3
Specialized instruction concerning the popular software is available to me
FC4
A specific person (or group) is available for assistance with hardware difficulties
4.26
1.83
2.62
5.55
15th
22nd
21st
12th
3.16
1.41
2.02
4.85
15th
22nd
21st
11th
Near-term consequences: complexity
CO1
Using a PC takes too much time from my normal work duties
CO2
Working with PCs is so complicated, it is difficult to understand what is going on
CO3
Using a PC involves too much time doing mechanical operations
(e.g., data input)
CO4
It takes too long to learn how to use a PC to make it worth the effort
JPF1
Use of a PC will have no effect on my work performance (score reversed)
JPF2
Use of a PC can decrease the time needed for my important job responsibilities
JPF3
Use of a PC can significantly increase the quality of output of my job
JPF4
Use of a PC can increase the effectiveness of performing work tasks (e.g., analysis)
JPF5
A PC can increase the quantity of output for same amount of effort
JPF6
Considering all tasks, use of PCs could assist on work to a great extent
Utilization
UT1
The intensity of job-related use (minutes per day, at work)
UT2
The frequency of PC use
UT3
The diversity of software packages used for work (number of packages)
responses with a few exceptions. The ®rst three items
in order of perceived level of agreement are AF1,
JPF3, and JPF4 and JPF6 (both taking the third position), while the ®rst three attitude items according to
mean responses are JPF3, AF1, and JPF4.
The above analyses are based on pooled responses.
The level of respondents' agreement may however
7.13
144.00
16.50
6.13
138.27
15.30
diverge when responses are partitioned on the basis of
organization size. Table 6 reports the results of such
analysis. The rankings of the attitude items differ from
one organization size to another. While respondents
from small companies agreed that AF1, EX1, and
JPF3 are the ®rst three in the order of importance,
those from medium companies preferred AF1, LT2,
198
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 6
Cross-size comparisons of means and indexes of agreements on importance of attitude items
Attitude items
Small companies n 100
means
adjusted index
of agreement
Medium companies n 24
means
SF1
3.87
3.77
3.29
SF2
SF3
SF4
AF1
AF2
AF3
CO1
CO2
CO3
CO4
JPF1
JPF2
JPF3
JPF4
JPF5
JPF6
LT1
LT2
LT3
LT4
LT5
LT6
FC1
FC2
FC3
FC4
TR
EX1
EX2
UT1
UT2
UT3
Ave UT
4.03
4.47
4.49
4.57
3.74
3.82
2.42
1.60
2.21
2.18
4.23
3.87
4.64
4.48
4.25
4.43
4.36
4.13
3.95
4.16
4.12
3.90
3.57
3.88
3.65
3.96
3.28
4.55
3.49
4.22
4.79
3.63
4.00
5.99
20.94
28.84
89.30
3.64
3.49
0.39
0.04
0.25
0.31
5.62
3.25
48.02
17.47
9.66
22.80
4.71
21.59
6.89
16.23
11.35
8.63
2.34
5.12
2.75
5.57
1.73
43.73
6.31
6.10
94.12
1.59
10.67
3.71
4.33
4.08
4.58
3.65
3.46
2.65
1.96
2.58
2.29
3.71
3.50
4.50
4.33
4.17
4.13
3.79
4.08
4.08
4.00
4.00
3.75
3.29
3.75
3.71
3.58
3.08
4.29
3.04
4.04
4.63
3.79
4.38
a
adjusted index
of agreement
1.78
2.71
19.25
4.55
1 (96%)a
2.63
1.75
0.54
0.20
0.37
0.27
2.00
2.43
21.08
21.08
9.17
9.17
5.25
1(79%)
1(79%)
9.17
7.50
3.75
1.42
3.72
4.22
2.44
1.25
21.08
0.63
3.33
1(92%)
2.89
23.00
Large companies n 26
means
3.31
4.23
4.57
4.37
4.65
4.27
3.80
2.50
1.81
2.00
1.88
4.42
3.81
4.85
4.77
4.24
4.58
4.07
4.11
4.31
4.31
4.15
3.88
3.92
3.88
3.79
4.00
3.69
4.46
3.46
4.27
4.85
3.50
3.69
adjusted index
of agreement
1.29
10.15
25.00
17.77
1 (96%)
17.77
2.40
0.78
0.13
0.18
0.18
10.15
3.04
1 (100%)
1 (100%)
6.72
1 (96%)
9.28
1 (65%)
19.47
21.23
1 (85%)
6.21
8.46
7.67
3.81
7.67
3.18
23.08
7.00
17.77
1 (96%)
1.57
4.85
Percentage of unequivocal responses to total responses.
LT3 and EX1. Respondents from large companies
suggest JPF3, and JPF4 as the most important item
followed by Af1 and JPF6.
7.1. Influence of respondents' profiles on perceptions
of PC utilization factors
To investigate the link between respondents' pro®les and their perceptions of PC utilization motivators,
we employed two different model speci®cations
(MANOVA and F-statistic approximations). MANOVA was used to evaluate the causes of the differing
responses from the respondents on the level of importance of each PC utilization factor. F-statistic approximation was used to test the hypothesis that each of the
respondents' pro®les has no overall effect on differences in their perceptions of a set of combined (dyad)
PC utilization factors. The results of the MANOVA
and Wilks' tests are shown in Table 7. Age seems to
have no in¯uence on all (but affect and facilitating
199
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 7
Influence of respondents profile on importance perceptions of PC utilization: ANOVA and F approximations results
Motivating factors
Access
Age
Education
Experience
Training
Panel A: (F-statistics)
Social
Affect
Complexity
Job performance facilitation
Long-term consequences
Facilitating conditions
Utilization
8.86c
3.03b
3.83c
2.59b
2.88b
5.81c
3.60c
0.09
1.23
0.91
0.86
1.69
2.83b
0.30
0.18
1.40
0.34
1.10
1.59
0.56
0.47
9.65c
3.01b
3.39b
2.74b
1.07
3.16b
10.41c
3.32b
1.84
2.75b
1.27
3.69c
6.40c
1.12
Panel B: Comparison of PC utilization motivators (Wilks' )
Interacting motivators:
Social/affect
Complexity/affect
Job performance facilitation/affect
Long-term consequences/affect
Facilitating conditions/affect
Job performance facilitation/long-term consequences
Complexity/long-term consequences
Facilitating conditions/long-term consequences
Social/long-term consequences
Complexity/social
Job performance facilitation/social
Facilitating conditions/social
Complexity/job performance facilitation
Facilitating conditions/job performance facilitation
Facilitating conditions/compexity
5.41c
3.10c
2.26b
2.28b
4.23c
1.98a
3.01c
3.85c
4.99c
5.76c
4.81c
5.38c
3.00c
3.90c
4.26c
0.76
1.12
0.97
1.51
2.13b
1.64
1.25
2.26b
1.01
0.51
0.55
1.63
0.93
1.97a
1.61
0.83
0.79
1.05
1.40
1.06
1.10
0.88
1.17
0.86
0.25
0.63
0.36
0.67
0.89
0.50
5.84c
2.97b
2.76b
2.32b
2.96b
1.58
2.01a
1.97a
4.82c
5.79c
5.77c
5.01c
2.90c
2.92c
2.81b
2.48b
2.17b
1.65
3.02c
4.13c
2.14b
3.01c
4.48c
3.01c
2.71c
1.92a
3.94c
1.87
3.52c
4.18c
a
p |z| 0.10.
p |z| 0.05.
c
p |z| 0.01.
The p-values reporting the signi®cance of the F-statistics for the hypothesis that each of the respondent pro®les has no overall effect on each of
the sets of PC utilization motivators relate to Wilks' tests. The same results were obtained when the (a) the Pillai's Trace, (b) Hotelling
Lawley Trace and (c) Roy's Greatest tests were conducted. The critical value of T 1.98.
b
conditions) of the PC utilization factors and each set of
the combined interaction factors. A respondent's level
of education seems to have no effect on any of the
factors suggested to in¯uence PC utilization or any of
the possible interaction factors.
PC utilization is in¯uenced strongly by degree of
PC Access (thus supporting H8), and experience (H9),
but not respondents' age (H10), education (H11)
and PC training (H7) and ownership of a PC. The
non-signi®cance of PC ownership is surprising but
understandable. Some respondents may own a PC
but may not use them. The PC may be acquired
for the use of their children. Some respondents who
do not own a PC may have one at home for of®cial
use. As a result, ownership of a PC is not a predictor
of PC utilization and is different from having access to
the PC.
7.2. The influence of attitude on PC utilization
Table 8 reports the intercorrelations among summarized group of attitude items, their mean values and
alphas. The results suggest that social factors, facilitating conditions, and Job Fit are signi®cantly correlated with PC utilization, while Affect is mildly
correlated with PC utilization.
Table 9 suggests that Social Factors, Affect, Job
Performance Facilitation and Facilitating Conditions
are signi®cant determinants of PC utilization. Complexity and Long-Term Consequences are not signi®-
200
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 8
Intercorrelations, means and alphas of summarized factors of computer utilization
Factors
1
1.
2.
3.
4.
5.
6.
7.
1.000
0.232 a 1.000
0.262 a 0.323 a 1.000
ÿ0.154 ÿ0.159 ÿ0.099
1.000
0.391 a 0.105
0.117 ÿ0.210 a 1.0000
0.234 a 0.477 a 0.385 a ÿ0.145 0.1780 1.0000
0.763 a 0.168 a 0.218 a ÿ0.116 0.2533 a 0.1113
a
Social
Affect
Job performance facilitation
Complexity
Facilitating conditions
Long-term consequences
Utilization
2
3
4
5
6
7
Mean
Standarddeviation
1.0000
4.17
4.05
4.32
2.13
3.76
4.03
4.20
0.62
0.70
0.67
0.83
0.88
0.68
0.72
0.70
0.44
0.68
0.
Research
The in¯uence of attitudes on personal computer utilization
among knowledge workers: the case of Saudi Arabia
Muhammad A. Al-Khaldi*, R.S. Olusegun Wallace
Department of Accounting and MIS, College of Industrial Management, King Fahd University of Petroleum and Minerals,
Dhahran 31261, Saudi Arabia
Received 22 April 1998; accepted 24 March 1999
Abstract
Since the introduction of personal computers (PCs) in the early 1980s, Saudi Arabia has made major investments in PCs to
match its rapidly growing economy. As a result, the PC business has become one of the fastest growing sectors in the Kingdom
of Saudi Arabia.
Our paper reports on the results of a study which investigates the relationships between end-users' attitudes and PC utilization among knowledge workers in the context of Saudi Arabia. To gain a better understanding of the factors that in¯uence the
use of PCs, we adopted Triandis' theory which suggests that behavior is determined by attitudes, social norms, habits and
expected consequences of behavior. Our study is based on previous efforts to test the theory's validity in Saudi Arabia.
Our results suggest that PC utilization is determined by individual attitudes, personal characteristics, such as PC experience,
facilitating conditions, such as PC access and social factors. We also observed that respondents to our questionnaire differ in
the level of importance they attribute to the factors hypothesized as in¯uencing PC utilization compared to Canadian
respondents in a previous study. # 1999 Elsevier Science B.V. All rights reserved.
Keywords: PC utilization; User attitudes; Social factors; Knowledge workers; Saudi Arabia
1. Introduction
One aim of our study is to ascertain the attitudes of
knowledge workers in Saudi Arabia [34] toward the
use of PCs, and to determine whether those attitudes
are similar to those reported by Thompson et al. [41,
42] for knowledge workers in Canada. There are
reasons for replicating the Canadian study in the
Kingdom of Saudi Arabia. Essentially, knowledge
about the factors that promote the usage of PCs is
useful to the global economyÐfar too important to be
*Corresponding author. Tel.: +966-3-860-2656; fax: +966-3860-3489; e-mail: makhaldi@kfupm.edu.sa
limited to the ®ndings from one national study. Different contexts allow us to understand variation across
the world. The Canadian context is different from the
Saudi context both from the point of view of economic
development and cultural orientation. First, Canada is
more developed than Saudi Arabia. In addition, Saudi
Arabia has only recently started to develop the educational, organizational and institutional systems that
have long existed in developed countries, such as
Canada. Second, Canada is essentially a secular community, while Saudi Arabia is an Islamic society with
a culture that manifests high power distance, uncertainty avoidant, collectivist, and femininity characteristics (along Hofstede's [23] cultural dimensions (see
0378-7206/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved.
PII: S - 0 3 7 8 - 7 2 0 6 ( 9 9 ) 0 0 0 1 7 - 8
186
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
[4, 5, 12]). Although the potential importance of PCs
in the social and economic development of Saudi
Arabia is recognized by Saudi authorities [11], the
need to uphold the tenets of Islam was probably
responsible for the delay (until January 1999) in
allowing public access through the PC to the enormous
potential of the internet within Saudi Arabia. Given
these societal differences, we would expect the PC
utilization attitudes of the respondents in Saudi Arabia
to differ from those of Canadian respondents. Another
aim of the study is to explore the importance of factors
(such as attitudesÐaffect, beliefs, behavior; societal
and facilitating conditions) in promoting the usage of
PCs in Saudi Arabia.
The Kingdom of Saudi Arabia has a population of
12.3 million citizens and 4.6 million foreign nationals,
mainly from the Indian sub-continent and East Africa.
More than 50 percent of the Saudi population are
under the age of 16. The country has a stable economy
and is a conducive environment for commerce and
business. Its commercial sector is relatively unregulated and the ®nancial sector is liberalized. Saudi
Arabia's microcomputer and minicomputer market
exhibits growth rates similar to the American market
[2]. As a result, it would be interesting to learn whether
the relative strength of attitudes towards PC usage in
Canada holds in Saudi Arabia. It is also interesting
because, in contrast to previous studies of the factors
capable of in¯uencing PC utilization in the Kingdom
(e.g., [1, 9, 10]), our study used practitioners rather
students as subjects of enquiry.
2. Conceptual framework and research questions
The link between end-user attitudes and PC utilization has occupied the attention of scholars in recent
years. The different frameworks include:
1. The theory of reasoned action (proposed by
Fishbein and Ajzen [18] and adopted by Thomp-
2.
3.
4.
5.
6.
son et al.) which seeks to investigate the reasoning
that lies behind the decision to utilize PCs. This
fact and the theory of perceived behavioral control
[6, 7, 8] suggest that PC usage can be predicted by
an individual's intention to use it. This intention is
determined by some weighted combination of the
individual's attitudes toward PC-related objects
(hardware, software, etc.).;
The theory of perceived behavioral control, based
on the individual's intention to perform a given
task, is an extension of the theory of reasoned
action to take account of behaviors over which
people have incomplete control;
Technology acceptance model proposed and
adopted by Davis et al. [14]. This model highlights
perceived ease of use and perceived usefulness of
the PC;
Expectancy model (proposed by Vroom [45] and
adopted by DeSanctis [16]) based on the belief that
the use of PCs leads to good performance, which in
turn leads to desired outcomes or the valence of
available outcomes;
Job diagnostic survey (utilized by Yaverbaum
[47]); and
Computer attitude scale (developed by Loyd and
Gressard [29] and used in experiments [20, 21, 30,
31] by them, Al-Jabri [3] and Al-Khaldi and AlJabri [9]). This framework examines four different
factorsÐanxiety or fear of PCs, con®dence in
ability to use or learn them, liking them, and their
usefulness.
Our study draws upon all these. Attitudes are determined by the individual's beliefs about the consequences of his or her behavior, based on the social
norms and mores. The relationship is illustrated in
Fig. 1.
In this context, user attitude is construed as a
learned predisposition toward PC-related objects.
Once the concept is de®ned thus, its operationalization
requires the identi®cation of all PC-related objects
Fig. 1. End-user attitude as a determinant of PC usage.
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
affecting end-user's attitudes and the development of a
measurement instrument to gauge the user's reaction.
While Fishbein and Ajzen considered all beliefs about
an act or behavior, Triandis [43, 44], in a modi®cation
of the Fishbein and Ajzen's theory, makes a clear
distinction between beliefs that link emotion to the act
(occurring at the moment of action) and beliefs that
link the act to future consequences. Triandis argues
that an individual's intended behavior is determined
by his or her feelings toward the object (Affect),
expected consequences of the behavior (cognition)
and what she/he should do (social factors).
The desire to understand end-users' behaviorÐ
their attitudes and perceptions toward the availability
of the PC, its costs and its ease of useÐcompels the
adoption of a multi-disciplinary approach. This
requires the use of a cumulative research tradition
by which theories and models borrowed from other
disciplines serve as a foundation [19, 27, 38]. For
example, a cognitive account of PC utilization is a
theory about its functional capacitiesÐthe things it
can doÐthat are involved during the completion of the
questionnaire. There are numerous ways of describing
the range and kinds of functional capacities involved
in any aspect of PC utilization but most scholars have
found it useful to describe these capacities in terms of
attitudes. However, probably because of the minimal
use of the knowledge accumulated in the social psychology literature, the empirical evidence on whether
PC utilization is in¯uenced by end-users' attitudes is
mixed and inconclusive; (e.g., see [40]). In addition,
little evidence relates to developing countries in general, and the Arabian Gulf Region in particular. To
facilitate our understanding of attitudinal factors of PC
utilization, we proposed the `working model' depicted
in Fig. 2.
The proposed model is a simpli®cation, as we are
not attempting to cover all relevant aspects of PC
utilization. The model groups many of the individual/
organizational motives for PC utilization into four
parts: (1) individual attitudes toward the PC; (2) social
factors capable of affecting PC usage; (3) individual
pro®les (age, experience, education, access and training of the respondents); and (4) organizational factors
which facilitate its use. The motives that are described
in the model as attitudes are disaggregated for analytical convenience into three portions, along the lines
suggested by Fishbein and Ajzen: affect, behavior, and
187
cognition. A theory of PC utilization would not be a
complete theory of attitude if the emotional and
intentional dimensions are excluded, thus previous
studies have examined the role of the affective and
cognitive faculties. In our study, these are referred to
as affect and behavior, respectively.
We separated the cognition motive into short-term
and long-term consequences. The ®rst has two dimensionsÐcomplexity (anxiety or fear of the PC) and
usefulness (how it ®ts the tasks and job of the enduser). Speci®cally, the study addresses two sets of
questions:
1. What are the factors that have the highest
in¯uence on the end-users' utilization of PCs in
Saudi Arabia? and what is the comparative
importance of attitudinal factors in different
countries? For example, do end-users in different
countries have similar attitudes? Do countryspeci®c conditions imply different PC usage?
2. Are there within-country differences in the importance of the attitudinal factors? For example, Culpan [13] has shown that there are perceptual
differences in the importance of attitudinal factors
across industries in south-central Pennsylvania. On
this basis, we investigate whether our respondents
differ in their perceptions of the degree of importance of the factors which may in¯uence PC utilization. We also investigate whether any
differences in the respondents' perceptions are
due to the differences in their pro®les.
3. Operationalization of constructs
To operationalize the constructs we adopted and
amended the instrument designed by Thompson et al.
The constructs are:
(1) Social factors. This set of factors deals with
within-organization socialization on the use of PCs
and comprises four items: (1) support of senior management of the department introducing PCs; (2) the
CEOs and managers' support of the use of PCs for
jobs; (3) the general support of the organization in the
introduction of PCs; and (4) the proportion of departmental workers who use PCs. These factors are presumed to capture the signals and messages that an enduser receives from his/her peers and others about what
188
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Fig. 2. Factors in¯uencing PC utilization.
they should do. They deal with an individual's internalization of work ethics and mores in organizations
and capture what individuals perceive as an acceptable
mode of conduct, given their generally accepted organizational culture. In line with Thompson et al., we
hypothesize that:
H1: There is a positive relationship between social
factors concerning the use of PCs and PC utilization.
Each of the four factors was measured by a simple
questionnaire item. For the ®rst three, the items were
scaled by a ®ve-point Likert-type scale (1 strongly
disagree to 5 strongly agree). The fourth item was
scaled using a ®ve-point scale representing different
proportions (1 10% or less to 5 90% or more).
(2) Affect factors represent respondents' affection
and disaffection for the PC. While we recognize
that affective and cognitive components of attitude
may overlap, we have adopted the strategy of separating the two as suggested by Thompson et al.
Affection for the PC is a function of an individual's
previous training and awareness of the PC. We
hypothesize that:
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
189
H2: There is a positive relationship between affect
and PC utilization.
H4: There is a positive relationship between perceived job ®t and PC utilization.
Affect relates to three items: (1) PCs make work
more interesting; (2) working with PCs is fun; and (3)
PCs are good for some tasks but not for the kind of task
I want. A ®ve-point Likert-type scale was used
(1 strongly disagree to 5 strongly agree).
(3) Cognitive factors. This set of factors deals
with the perceived consequences of adopting a PC
for the individual, such as `working with the PC is
complicated or it takes too long to learn how to use a
PC to make it worth the effort.' As Thompson et al.
suggest, the perceived consequences construct is
consistent with the expectancy theory of motivation, proposed by Vroom, which suggests that individuals evaluate the consequences of their behavior
in terms of potential rewards and base their actions
on the attractiveness of the rewards. Perceived
consequences may be positive or negative, just as
they can be high or low. The consequences may be
felt immediately on the use of a PC or may produce
a valence whose effect may extend beyond the shortterm to the long-term. We examine two perceived
short-term consequences and one perceived long-term
consequence.
3.2. Long-term consequences
3.1. Short-term consequences
(a) Complexity. Because complexity refers to the
dif®culty that an individual may experience while
using the PC, we hypothesize that:
H3: There is a negative relationship between perceived complexity and PC utilization.
Four complexity items were given to the respondents and a ®ve-point Likert-type scale was used to
capture their opinions on each (1 strongly disagree
to 5 strongly agree).
(b) Usefulness or job performance facilitation
(JPF). This set of perceived behavioral control factors
was captured, using six items that describe the potential outcomes and valences from using a PC; e.g., the
use of a PC can decrease the time needed for my
important job responsibilities. These refer to the PCs
potential for enhancing the individual's job performance. We hypothesize that:
The positive and negative effects of PC utilization
can often be felt on work performed long after the PC
was introduced. Such factors were captured with six
items, using a ®ve-point Likert-type scale. Respondents were asked for their opinion on the perceived
understanding of the long-term effect of using the PC.
We hypothesize that:
H5: There is a positive relationship between perceived long-term consequences of PC use and PC
utilization.
The six types of effect on job characteristics are: the
level of challenge of the respondents' job; the opportunity for preferred future jobs; the amount of variety
on the job; the opportunity for more meaningful work
in the future; the ¯exibility of changing jobs in the
future; and the opportunity for job security. The effects
that the respondents were asked to consider include
the potential for increase, decrease, or no change in
each of the six job characteristics.
(4) Facilitating factors refer to the availability of
support systems. Six items were also used to operationalize these factors. We adopt Triandis's de®nition
of facilitating factors as the ``objective factors, `out
there' in the environment, that several judges or
observers can agree make an act easy to do.'' We
assumed that there was a positive association between
supporting systems and PC utilization. We did not
factor into our study conditions which impede PC
utilization. As a result, we hypothesize that:
H6: There is a positive relationship between supporting facilitating conditions and PC utilization.
(5) PC utilization factors were captured using three
dimensions: the intensity of PC use; frequency of PC
use and diversity of software packages used. Intensity
was measured by the number of minutes devoted to the
use of PC per day at work. Frequency of use was
measured by four categories, ranging from less than
once per week to several times a day. The diversity of
use was measured by counting the number of software
190
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
packages optionally used at work, which ranging from
1 to 5.
(6) Other factors investigated include PC training,
PC experience, and age. Training was indicated by
Thompson et al. as an element that needs to be
investigated with regard to PC attitude and it was
later investigated by them. Respondents that are
trained in the use of the PC would most probably
tend to attribute greater importance to PC utilization
motivators than those who are not. The more exposed
to PC training, the more likely that there would be
perceived positive long-term consequences. As a
result, we hypothesize that:
H7: There is a positive relationship between PC
training and PC utilization.
Other studies have found a signi®cant effect of PC
access and experience on reducing PC anxiety and
enhancing its utilization. We predict PC utilization to
increase as the individual respondent gains more
access to the PC, or as he gains more experience
working with the PC. PC familiarity and experience
can reduce fears and negative attitudes toward the PC
and can encourage PC utilization. We hypothesize
that:
H8: There is a positive relationship between the
degree of access to the PC and PC utilization.
H9: There is a positive relationship between the
extend of PC experience and PC utilization.
Given the relatively nascent usage of PCs in the
Kingdom of Saudi Arabia, it is possible that the level
of education and age may be major factors in the
utilization of the PC. We predict that the level of
education would be positively associated with PC
utilization and that age would be negatively related
to PC utilization. Younger employees are likely to
have had some training in the use of PCs in their
education, while older people would have gone to
school at a time when PC education was not available.
These speculations form the bases of following
hypotheses:
H10: There is a positive relationship between education level and PC utilization.
H11: There is a negative relationship between age
and PC utilization.
3.3. Organization size
Organization size has been suggested as a predictor
of PC adoption in organizations [15, 17, 25, 35, 36].
On the basis of empirical research on ®rms, we suggest
that organization size may be equally signi®cant
for PC utilization and that it may have a stronger
moderating in¯uence than individual attitudes on
PC utilization by knowledge workers. The larger a
®rm is, the greater are the prospects of computerization. If this is true, this challenges one of the most
central orthodoxies of PC utilization studies, and
implies that those seeking to promote PC utilization
ought, in choosing the method of motivating PC
utilization, to give as much, or more weight to
organization size, as to other motivating factors.
Firms with substantial size can attain more effective
transition from manual to PC systems through investment in training. Growth in size generates pressures
for computerization.
According to Raymond [37], smaller ®rms can
hardly afford the enormous costs of PC utilization
(employment of persons with specialized knowledge,
training staff, transformation of manual to PC systems,
acquisition). The activities of small ®rms are often
below the level that is optimal for computerization.
While previous studies have examined the factors that
in¯uence PC utilization in small ®rms within a country
(e.g., New Zealand, Saudi Arabia and Taiwan [24]),
the studies have often assumed that the factors that
promote PC utilization in small ®rms would be different from those in large ®rms. This assumption is
based on intuitive and reasoned conclusion rather than
empirical investigation. We use number of employees
to measure organization size. This has been used as a
measure of organization size in several studies in
accounting (e.g., [46]).
4. Methodology
4.1. Sample
The study was conducted in the Eastern Province of
Saudi Arabia. The survey locality is typical of the
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
country. It is the headquarters of the dominant ®rms in
the Kingdom and has a great number of organizations
(light industries, retail outlets and many government
administrative of®ces). Most of the respondents work
in semi-government pro®t-seeking ®rms and private
®rms located in Dhahran and Dammam, the cities with
the largest concentration of knowledge workers in the
Eastern Province. A sample of 200 knowledge workers was targeted. Knowledge workers whose opinions
are pooled for this study include accountants, treasurers and controllers, general managers, engineers,
production analysts, and corporate planners. They
generally use low-end applications software and not
decision support or expert systems. However, the
knowledge workers who participated in the study as
end-users come within the ®rst three categories of the
six end-user categories identi®ed by Rockart and
Flannery [39]:
1. non-programming end-users, who access data
through predeveloped menu-driven software
packages;
2. command level end-users, who generate unique
reports for their own purposes, usually with simple
query languages; and
3. end-user programmers, who utilize command and
procedural languages to access, manipulate, and
process data for their personal information needs.
In total, after one follow-up (and telephone communication) with the contact persons in each participating
®rm, 151 (75.5%) useful responses to the questionnaire were collected from the participants. Every
questionnaire which achieves less than 100 percent
response rate has a potential non-response bias problem. Like most researchers, we have addressed this
problem in two ways.
1. By attempting to obtain as high a response rate as
possible. However, questionnaires dealing with
sensitive personal issues may be more likely to
result in non-response. Users who are not
comfortable with the PC may be reluctant to
respond, while competent users may be particularly interested in the subject and willing to
participate in the study. Here, we attempted to
reduce non-response bias by using a follow-up
procedure and the coordinator within a participating ®rm to chase potential non-respondents.
191
2. By estimating qualitatively the potential effect of
non-response on the ®ndings. On this basis, we
conducted a comparative analysis of responses by
date of response (or date of receipt of responses).
This analysis is based on the presumption that late
responders are reasonable `surrogates' of nonrespondents. The response rate of 75.5% is the
average of responses from the entire sample; the
response rates from participating ®rms vary from a
low of 60 percent to a high of 100 percent. Every
effort was made to minimize the occurrence of nonresponse within a returned questionnaire. Where
one was noticed, the individual ®rm from where the
incomplete questionnaire came was consulted and
the contact person in that ®rm was asked to trace
the individual respondent to get the required
response.
The questionnaire used to elicit opinions from respondents is a slightly modi®ed version of an instrument
used by Thompson, et al. for Canadian surveys. The
present study differs from these studies in the following ways. First, it differs in terms of the spread of
respondents. Whereas the Canadian study collected
responses from participants drawn from a large multinational manufacturing ®rm, the respondents to our
study were drawn from 10 of the leading ®rms in the
Eastern Province of Saudi Arabia. Second, the questionnaires were not completed, using the DISKQ
technique adopted by Thompson et al. Instead, we
employed a paper and pencil technique. We concluded
that an interactive questionnaire was not suitable for
the present environment of Saudi Arabia. The use of
the method would affect the response rate in an
environment where people are not used to it.
5. Procedure
In each participating ®rm (see Table 1 for the
characteristics of the participating ®rms), a coordinator was appointed to arrange the distribution of questionnaires to (and collection of completed ones from)
knowledge workers. The survey envelope included a
cover letter from the researchers and from the coordinator in the participating ®rm, introducing the purpose
of the study and guaranteeing con®dentiality of individual responses. It also indicated that the results
192
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 1
Characteristics of respondent companies
Number of
employees
Type of
firm
Under 50
51±200
Over 200
Small
Medium
Large
Industry classification
1. Manufacturing
2. Construction
3. Agriculture
4. Services
5. Oil and energy
6. Trade, retailing, leasing
No. of
firms
100
24
26
(%)
66.67
16.00
17.33
100.00
40.6
4.2
0.7
25.2
18.9
10.4
100.0
would not refer to individual responses but would be
published in summary form only.
6. Aggregation of responses
The instrument is structured in a way that asked the
respondents (see Table 2 for their pro®le) to indicate
the level of their agreement with perceptual anchors
on the level of importance of each of the suggested
factors capable of promoting PC utilization. The
survey instrument used a Likert scale (varying from
`strongly agree' 5 to `strongly disagree' 1, with
room for equivocation in the middle, i.e.,
`neutral' 3). While factors are not independent,
respondents were asked to treat each factor as independent of the others and to assign as many `5s', `4s',
`3s' etc., as they felt were warranted by the factors
included in the survey instrument; i.e., not to rank
them. The responses to the items within each group
were aggregated to produce indexes for each grouped
variable. For example, the PC utilization index was
derived by averaging the means of the responses to
each of the three items that make up PC utilization.
The validity of the survey instrument was assessed
by a factor analysis. Table 3 contains rotated factor
matrix (varimax) with eight speci®ed factor loadings
on their own constructs than on others. This is the case
with rotated structure. However, there is one exception. Item JPF1 fails to load highly on Factor 2, which
represents the construct job performance facilitation
but loads highly on Factor 8, which captures the affect
items. As a result, the rotated structure appears to be
Table 2
Profile of respondent individuals
Age
No.
%
20±25 years
26±30 years
31±40 years
41±50 years
51 and above
Total
10
29
73
33
5
150
6.7
19.3
48.7
22.0
3.3
100.0
Access to a PC
No access
Low
Average
High
Total
0
12
65
74
151
0.0
7.9
43.1
49.0
100.0
Experience in using the PC
None
Low
Moderate
High
Total
2
31
88
29
150
0.13
20.67
58.67
19.33
100.00
No.
%
Ownership of a PC
Yes
No
Total
79
71
150
52.7
47.3
100.0
Educational background
Less than High school
High school
Some years in college
Bachelor's degree
Graduate degree
Total
1
10
15
85
39
150
0.06
6.67
10.00
56.67
26.00
100.00
193
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 3
Rotated factor matrix of attitude items
Attitude items
1
2
3
4
5
6
7
8
LT1
LT2
LT3
LT4
LT5
LT6
JPF1
JPF2
JPF3
JPF4
JPF5
JPF6
FC1
FC2
FC3
FC4
SF1
SF2
SF3
SF4
COM1
COM2
COM3
COM4
TR1
TR2
UT1
UT2
UT3
AF1
AF2
AF3
0.648
0.784
0.758
0.690
0.655
0.675
0.125
ÿ0.041
0.158
0.191
0.204
0.243
0.215
ÿ0.015
0.052
ÿ0.020
ÿ0.122
0.239
0.076
0.230
0.118
ÿ0.078
ÿ0.127
ÿ0.069
0.158
0.190
0.012
ÿ0.004
0.109
0.225
0.336
0.241
0.148
0.041
0.233
0.122
0.111
0.154
0.172
0.431
0.818
0.787
0.664
0.753
0.026
0.039
0.092
ÿ0.040
0.091
0.218
ÿ0.028
0.075
ÿ0.232
ÿ0.030
ÿ0.021
ÿ0.070
0.046
0.024
0.203
0.053
0.014
0.160
0.198
0.046
0.027
ÿ0.004
0.055
ÿ0.032
0.185
0.066
0.170
ÿ0.049
ÿ0.046
0.113
0.075
0.037
0.646
0.761
0.744
0.785
ÿ0.014
0.292
0.093
0.381
ÿ0.118
0.035
ÿ0.093
ÿ0.279
0.248
0.270
0.141
0.097
0.088
ÿ0.014
0.228
ÿ0.098
ÿ0.055
0.143
ÿ0.030
0.235
0.131
0.124
0.375
0.038
0.087
0.092
0.030
0.056
0.168
0.064
0.140
0.132
0.521
0.695
0.787
0.677
0.068
ÿ0.133
ÿ0.067
0.138
0.010
0.018
0.190
0.041
0.020
0.364
0.029
ÿ0.013
0.054
ÿ0.100
0.015
ÿ0.183
ÿ0.077
0.079
ÿ0.193
0.323
ÿ0.066
ÿ0.056
ÿ0.043
ÿ0.131
ÿ0.010
ÿ0.141
0.046
ÿ0.102
ÿ0.099
0.033
ÿ0.045
ÿ0.113
0.565
0.720
0.775
0.537
0.021
ÿ0.042
0.033
ÿ0.005
0.089
0.182
0.174
ÿ0.225
0.018
0.108
ÿ0.145
0.212
0.079
0.195
ÿ0.203
0.105
0.100
0.047
ÿ0.166
0.007
0.240
0.214
0.100
ÿ0.000
0.024
0.022
0.069
ÿ0.056
ÿ0.179
0.075
ÿ0.158
0.276
0.849
0.838
ÿ0.108
0.036
0.183
0.046
ÿ0.286
ÿ0.161
ÿ0.064
0.089
0.098
ÿ0.074
ÿ0.041
0.053
ÿ0.027
ÿ0.142
0.895
0.176
0.100
0.062
0.006
0.176
0.175
0.005
0.459
0.139
0.146
ÿ0.109
0.136
0.013
0.050
ÿ0.408
0.007
0.000
0.737
0.681
0.494
ÿ0.015
ÿ0.221
0.240
0.121
0.145
0.026
0.116
0.279
ÿ0.145
0.504
0.095
ÿ0.000
0.017
0.052
0.223
ÿ0.070
0.034
ÿ0.060
0.175
ÿ0.006
ÿ0.051
0.177
0.123
ÿ0.466
ÿ0.008
0.026
ÿ0.155
ÿ0.064
ÿ0.014
0.111
ÿ0.081
0.425
0.570
0.374
0.599
consistent with the underlying theoretical constructs
of the survey instrument. In further statistical analysis
of the job performance facilitation items, JPF1 was not
included. For example, the Cronbach's reported for
the job performance facilitation does not include JPF1.
The internal consistency of the eight scales was
assessed using Cronbach's alpha ( ). We report two
inter-rater correlation coef®cients (ICCs) that are
estimates of reliability and validity. ICC(1) is an
estimate of the degree to which responses on each
item are similar, is reported in Table 4, and ICC(2) is
an estimate of the reliability of the mean scores [22], is
reported in the last column of Table 8. ICC(1) and
ICC(2) address two different issues [33]. If two
respondents were randomly sampled from the same
group and their two sets of scores correlate, the
resulting correlations would approximately equal
ICC(2) which is scaled as zero (0) when the observable
level of agreement is minimal and one (1) when there
is perfect agreement. For intermediate values, Landis
and Koch [28] suggest the following interpretations:
below 0.0 poor agreement; 0.00±0.20 slight
agreement; 0.21±0.40 fair agreement; 0.41±
0.60 moderate agreement; 0.61±0.80 substantial
agreement; and 0.81±1.00 almost perfect agreement. ICC(2) ratings of 0.60 and above suggest that
one can reject the hypothesis that respondents were
scoring an item randomly and that one can conclude
that acceptable levels of mean score reliability exist
[32]. ICC(1) indicates the extent to which respondents
within the same group assign the same psychological
meaning to, or agree in their perceptions of, the
194
Alpha
Intercorrelations of social factors items
SF1
SF2
SF3
SF1
SF2
SF3
SF4
1.000
0.286b
0.292b
0.2131a
Intercorrelations of affect
AF1
AF1
1.000
AF2
0.342b
AF3
0.2550a
1.000
0.586b
0.5293b
1.000
0.491b
factors
AF2
AF3
1.000
0.166a
Mean
Standard
deviation
Mean rank
SF4
Mean
1.000
1.000
Intercorrelations of near-term consequences: complexity
CO1
CO2
CO3
CO4
CO1
1.000
CO2
0.286b
1.000
0.420b
1.000
CO3
0.389b
CO4
0.273b
0.309b
0.351b
1.000
Intercorrelations of near-term consequences: job fit (usefulness)
UF1
UF2
UF3
UF4
UF5
JPF1
1.000
JPF2
0.021
1.000
JPF3
0.099
0.160
1.000
0.669
JPF4
0.196
0.140
0.705b
1.000
JPF5
0.196
0.143
0.483b
0.530b
1.000
b
b
JPF6
0.319
0.151
0.655
0.655b
0.532b
Canada
Standard
Deviation
Rank
0.776
0.599
0.610
0.656
3.68
4.01
4.47
4.40
1.22
1.19
0.81
0.84
21st
13th
4th
5th
1.88
3.04
3.81
4.24
0.86
1.21
1.30
1.05
28th
25th
17th
4th
0.285
0.406
0.510
4.58
3.81
3.76
0.62
1.12
1.21
2nd
18th
20th
4.11
4.22
n/a
0.78
0.78
n/a
10th
5th
n/a
0.628
0.605
0.549
0.633
2.46
1.70
2.23
2.15
1.35
0.95
1.67
1.23
28th
31st
29th
30th
4.39
4.47
3.49
4.30
0.94
0.89
1.14
1.07
2nd
1st
19th
3rd
0.780
0.795
4.65
0.656
0.692
0.646
4.18
3.80
0.70
4.51
4.23
4.40
1.23
1.36
1st
0.83
1.05
0.83
8th
19th
4.16
3rd
7th
5th
4.11
3.92
1.02
4.19
4.13
4.07
1.17
1.27
7th
0.97
1.00
0.87
9th
14th
UF6
1.000
6th
8th
11th
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 4
Intercorrelations, means and mean rankings of the importance of attitude items in Saudi Arabia compared with Canadian rankings from the [14] study (See Table 5 for description
of variables)
Intercorrelations of long-term consequences
LT1
LT2
LT3
LT1
1.000
LT2
0.465b
1.000
LT3
0.488b
0.537b
1.000
LT4
0.409b
0.557b
0.391b
b
b
0.550
0.440b
LT5
0.398
LT6
0.2774b
0.5690b
0.4033b
LT4
LT5
LT6
3.88
4.12
4.03
4.16
4.11
3.87
1.06
0.81
0.96
0.84
0.85
1.01
15th
10th
12th
9th
11th
16th
3.39
3.20
2.74
3.23
3.01
3.22
1.17
1.25
1.32
1.23
1.19
1.262
20th
23rd
27th
22nd
26th
4th
1.000
0.786
0.715
0.747
0.731
3.56
3.86
3.68
3.91
1.16
1.02
1.16
1.11
23rd
17th
21st
14th
3.59
3.88
3.57
4.00
1.30
1.25
1.25
1.12
17th
15th
18th
12th
Intercorrelations of training and experience factors
TR1
TR2
EX1
EX2
TR1
1.000
TR2
0.844b
1.000
EX1
ÿ0.046
ÿ0.034
1.000
EX2
0.079
0.084
ÿ0.016
1.000
0.056
0.050
0.603
0.506
3.32
3.41
2.96
7.29
1.43
1.45
0.67
4.87
25th
24th
26th
n/a
n/a
n/a
3.99
3.22
n/a
n/a
1.39
1.08
n/a
n/a
13th
24th
0.350
0.449
0.627
4.20
4.77
3.63
1.04
0.54
1.31
3.56
4.33
3.57
1.38
1.03
1.27
Intercorrelations of facilitating conditions
FC1
FC2
FC3
FC1
1.000
FC2
0.421b
1.000
0.576b
1.000
FC3
0.430b
b
FC4
0.462
0.605b
0.474b
Intercorrelations of utilization
UT1
UT2
UT1
1.000
UT2
0.456b
1.000
UT3
0.289b
0.212a
a
b
p 0.05.
p 0.01.
1.000
0.605b
0.5567b
1.000
0.4279b
1.0000
FC4
0.506
UT3
1.000
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
0.835
0.797
0.823
0.807
0.813
0.824
195
196
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
relevance of research results on a computer utilization
factor. ICC(1) compares the between sample sum of
squares to the total sum of squares from the results of a
one-way analysis of variance. In past research, ICC(1)
values have ranged from 0 to 0.5, with a median of
0.12 [26]. There are no de®nite guidelines on acceptable ICC(1) values.
The responses on each factor were averaged (aggregated) and these were used to determine the relative
importance of each factor. This aggregation of
responses suggests that respondents were agreed on
mean scores though the reported standard deviations
challenge the assumption of convergence. In fact, the
mean scores do not reveal variability across situations,
such as age of respondents or size of the ®rm in which
a respondent is employed; nor do they permit prediction of speci®c behaviors in given situations. To unveil
the fac,ade of convergence from the pooled data, we
analyzed the responses in two different ways. First, we
used the responses showing agreement with the suggestion that a factor is important and those not agreeing with the suggestion of importance to derive an
index of agreement that was introduced by Herbert
and Wallace [22] in their study of the perceptions of
U.K. Chief Finance Of®cers on corporate ®nance
research topics. An index of agreement is the ratio
of total agreeing responses (i.e., 5s and 4s for positive
questions or 1s and 2s for negative questions) to total
disagreeing response (i.e., 1s and 2s for positive
questions and 5s and 4s for negative questions).
The index excludes equivocal (i.e., `not sure' 3)
responses which vary from item to item. To make an
index of agreement comparable in this study, each
index was adjusted to re¯ect the ratio of unequivocal
responses to all responses by multiplying that ratio
with the index of agreement.
Second, we disaggregated the responses on the basis
of organization size into large, medium, and small
®rms. Although the pooled data suggests that many of
the factors warranted more research, signi®cant differences may exist among ®rms of different sizes.
7. Results
The ®rst question is the comparative attitudes of
Canadian and Saudi respondents toward the utilization
of the PC. Since the questionnaire for Saudi respon-
dents was similar to that for the Canadian respondents,
we compared the mean responses from each. The
settings, situations and contexts are our referential
scopes because it is dif®cult to delve into the minds
of our subjects and to substantiate what seems to be
within the realm of mere conjecture. The process of
our reference must, therefore, rely heavily on perceived behavior and we have been concerned exclusively with organizationally constructed modes of
behavior. The results of the comparison of the mean
responses from the Saudi and Canadian respondents
are reported in Table 4.
While the three highest levels of Saudi respondents'
agreement with our suggestible questions were,
respectively, in respect of (1) the ®tness of the PC
to respondents' job (Question JPF3 with mean
response of 4.65, 0.67), (2) affect (AF1, mean
response of 4.58, 0.29) and (3) job ®tness (JPF4,
mean response of 4.51, 0.66), those of the Canadian respondents were mainly with the suggestible
questions on complexity: (1) Question CO2 with mean
response of 4.47, (2) Question CO1, mean response of
4.39, and (3) CO4, mean response of 4.30. It is
surprising that Saudis perceived the PC as relatively
less dif®cult to understand than Canadians. If this
observation is true, we would expect to ®nd that the
level of PC utilization in the two studies would show
that the Canadians would reveal a lower PC utilization
than the Saudis. The results con®rm this expectation
because on each of the PC utilization constructs, the
mean responses from the Saudis were greater than
those from the Canadians.
The results from the use of a different procedure for
aggregating the responses from the Saudi respondents
are similar to those from the use of mean responses,
except that affect item AF1 and job performance
facilitation item JPF3 swapped positions, with job
performance facilitation item JPF6 tying with job
performance facilitation item JPF4 as the items with
the third highest level of respondent agreement with
the suggestible questions.
To test the robustness of our ®ndings on the attitude
items which are accorded highest level of importance
on the basis of mean responses, we developed an index
of agreement along the lines suggested in earlier
discussion. Table 5 reports these indexes of agreement. The ranking of attitude items on the basis of
indexes of agreement is similar to the one from mean
197
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 5
Pooled sample indexes of agreement on the level of importance accorded attitude items (n 151)
Attitude
items
Index of
agreement
Rank
Adjusted index
of agreement
Rank
Social
SF1
SF2
SF3
SF4
The proportion of departmental co-workers who use a PC
The senior management of this business unit has been helpful in introducing PCs
My boss is very supportive of PC use for my job
In general, the organization has supported the introduction of PCs
2.94
5.90
22.67
16.50
20th
9th
5th
6th
2.39
5.43
21.32
15.30
20th
9th
5th
6th
Affect
AF1
AF2
AF3
PCs make work more interesting
Working with a PC is a fun
PCs are okay for some tasks but not the kind of task I want (score reversed)
142.00
4.82
3.38
1st
14th
16th
135.37
4.08
2.88
1st
14th
17th
0.54
0.09
0.30
24th
27th
26th
0.47
0.08
0.25
24th
27th
26th
0.30
5.00
3.35
48.33
23.17
9.62
23.17
25th
13th
17th
2nd
3rd
7th
3rd
0.28
4.80
3.02
47.69
22.39
8.91
22.25
25th
12th
16th
2nd
3rd
7th
4th
Long-term consequences
LT1
Use of a PC will increase the level of challenge on my job
LT2
Use of a PC will increase the opportunity for preferred future job assignments
LT3
Use of a PC will increase the amount of variety on my job
LT4
Use of a PC will increase the opportunity for more meaningful work
LT5
Use of a PC will increase the flexibility of changing jobs
LT6
Use of a PC will increase the opportunity to gain job security
5.71
8.39
5.63
3.04
3.07
0.77
10th
8th
11th
19th
18th
23rd
4.61
7.49
5.22
2.63
2.57
0.52
13th
8th
10th
18th
19th
23rd
Facilitating conditions
FC1
Guidance is available to me in the selection of hardware and software
FC2
A specific person (or group) is available for assistance with software difficulties
FC3
Specialized instruction concerning the popular software is available to me
FC4
A specific person (or group) is available for assistance with hardware difficulties
4.26
1.83
2.62
5.55
15th
22nd
21st
12th
3.16
1.41
2.02
4.85
15th
22nd
21st
11th
Near-term consequences: complexity
CO1
Using a PC takes too much time from my normal work duties
CO2
Working with PCs is so complicated, it is difficult to understand what is going on
CO3
Using a PC involves too much time doing mechanical operations
(e.g., data input)
CO4
It takes too long to learn how to use a PC to make it worth the effort
JPF1
Use of a PC will have no effect on my work performance (score reversed)
JPF2
Use of a PC can decrease the time needed for my important job responsibilities
JPF3
Use of a PC can significantly increase the quality of output of my job
JPF4
Use of a PC can increase the effectiveness of performing work tasks (e.g., analysis)
JPF5
A PC can increase the quantity of output for same amount of effort
JPF6
Considering all tasks, use of PCs could assist on work to a great extent
Utilization
UT1
The intensity of job-related use (minutes per day, at work)
UT2
The frequency of PC use
UT3
The diversity of software packages used for work (number of packages)
responses with a few exceptions. The ®rst three items
in order of perceived level of agreement are AF1,
JPF3, and JPF4 and JPF6 (both taking the third position), while the ®rst three attitude items according to
mean responses are JPF3, AF1, and JPF4.
The above analyses are based on pooled responses.
The level of respondents' agreement may however
7.13
144.00
16.50
6.13
138.27
15.30
diverge when responses are partitioned on the basis of
organization size. Table 6 reports the results of such
analysis. The rankings of the attitude items differ from
one organization size to another. While respondents
from small companies agreed that AF1, EX1, and
JPF3 are the ®rst three in the order of importance,
those from medium companies preferred AF1, LT2,
198
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 6
Cross-size comparisons of means and indexes of agreements on importance of attitude items
Attitude items
Small companies n 100
means
adjusted index
of agreement
Medium companies n 24
means
SF1
3.87
3.77
3.29
SF2
SF3
SF4
AF1
AF2
AF3
CO1
CO2
CO3
CO4
JPF1
JPF2
JPF3
JPF4
JPF5
JPF6
LT1
LT2
LT3
LT4
LT5
LT6
FC1
FC2
FC3
FC4
TR
EX1
EX2
UT1
UT2
UT3
Ave UT
4.03
4.47
4.49
4.57
3.74
3.82
2.42
1.60
2.21
2.18
4.23
3.87
4.64
4.48
4.25
4.43
4.36
4.13
3.95
4.16
4.12
3.90
3.57
3.88
3.65
3.96
3.28
4.55
3.49
4.22
4.79
3.63
4.00
5.99
20.94
28.84
89.30
3.64
3.49
0.39
0.04
0.25
0.31
5.62
3.25
48.02
17.47
9.66
22.80
4.71
21.59
6.89
16.23
11.35
8.63
2.34
5.12
2.75
5.57
1.73
43.73
6.31
6.10
94.12
1.59
10.67
3.71
4.33
4.08
4.58
3.65
3.46
2.65
1.96
2.58
2.29
3.71
3.50
4.50
4.33
4.17
4.13
3.79
4.08
4.08
4.00
4.00
3.75
3.29
3.75
3.71
3.58
3.08
4.29
3.04
4.04
4.63
3.79
4.38
a
adjusted index
of agreement
1.78
2.71
19.25
4.55
1 (96%)a
2.63
1.75
0.54
0.20
0.37
0.27
2.00
2.43
21.08
21.08
9.17
9.17
5.25
1(79%)
1(79%)
9.17
7.50
3.75
1.42
3.72
4.22
2.44
1.25
21.08
0.63
3.33
1(92%)
2.89
23.00
Large companies n 26
means
3.31
4.23
4.57
4.37
4.65
4.27
3.80
2.50
1.81
2.00
1.88
4.42
3.81
4.85
4.77
4.24
4.58
4.07
4.11
4.31
4.31
4.15
3.88
3.92
3.88
3.79
4.00
3.69
4.46
3.46
4.27
4.85
3.50
3.69
adjusted index
of agreement
1.29
10.15
25.00
17.77
1 (96%)
17.77
2.40
0.78
0.13
0.18
0.18
10.15
3.04
1 (100%)
1 (100%)
6.72
1 (96%)
9.28
1 (65%)
19.47
21.23
1 (85%)
6.21
8.46
7.67
3.81
7.67
3.18
23.08
7.00
17.77
1 (96%)
1.57
4.85
Percentage of unequivocal responses to total responses.
LT3 and EX1. Respondents from large companies
suggest JPF3, and JPF4 as the most important item
followed by Af1 and JPF6.
7.1. Influence of respondents' profiles on perceptions
of PC utilization factors
To investigate the link between respondents' pro®les and their perceptions of PC utilization motivators,
we employed two different model speci®cations
(MANOVA and F-statistic approximations). MANOVA was used to evaluate the causes of the differing
responses from the respondents on the level of importance of each PC utilization factor. F-statistic approximation was used to test the hypothesis that each of the
respondents' pro®les has no overall effect on differences in their perceptions of a set of combined (dyad)
PC utilization factors. The results of the MANOVA
and Wilks' tests are shown in Table 7. Age seems to
have no in¯uence on all (but affect and facilitating
199
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 7
Influence of respondents profile on importance perceptions of PC utilization: ANOVA and F approximations results
Motivating factors
Access
Age
Education
Experience
Training
Panel A: (F-statistics)
Social
Affect
Complexity
Job performance facilitation
Long-term consequences
Facilitating conditions
Utilization
8.86c
3.03b
3.83c
2.59b
2.88b
5.81c
3.60c
0.09
1.23
0.91
0.86
1.69
2.83b
0.30
0.18
1.40
0.34
1.10
1.59
0.56
0.47
9.65c
3.01b
3.39b
2.74b
1.07
3.16b
10.41c
3.32b
1.84
2.75b
1.27
3.69c
6.40c
1.12
Panel B: Comparison of PC utilization motivators (Wilks' )
Interacting motivators:
Social/affect
Complexity/affect
Job performance facilitation/affect
Long-term consequences/affect
Facilitating conditions/affect
Job performance facilitation/long-term consequences
Complexity/long-term consequences
Facilitating conditions/long-term consequences
Social/long-term consequences
Complexity/social
Job performance facilitation/social
Facilitating conditions/social
Complexity/job performance facilitation
Facilitating conditions/job performance facilitation
Facilitating conditions/compexity
5.41c
3.10c
2.26b
2.28b
4.23c
1.98a
3.01c
3.85c
4.99c
5.76c
4.81c
5.38c
3.00c
3.90c
4.26c
0.76
1.12
0.97
1.51
2.13b
1.64
1.25
2.26b
1.01
0.51
0.55
1.63
0.93
1.97a
1.61
0.83
0.79
1.05
1.40
1.06
1.10
0.88
1.17
0.86
0.25
0.63
0.36
0.67
0.89
0.50
5.84c
2.97b
2.76b
2.32b
2.96b
1.58
2.01a
1.97a
4.82c
5.79c
5.77c
5.01c
2.90c
2.92c
2.81b
2.48b
2.17b
1.65
3.02c
4.13c
2.14b
3.01c
4.48c
3.01c
2.71c
1.92a
3.94c
1.87
3.52c
4.18c
a
p |z| 0.10.
p |z| 0.05.
c
p |z| 0.01.
The p-values reporting the signi®cance of the F-statistics for the hypothesis that each of the respondent pro®les has no overall effect on each of
the sets of PC utilization motivators relate to Wilks' tests. The same results were obtained when the (a) the Pillai's Trace, (b) Hotelling
Lawley Trace and (c) Roy's Greatest tests were conducted. The critical value of T 1.98.
b
conditions) of the PC utilization factors and each set of
the combined interaction factors. A respondent's level
of education seems to have no effect on any of the
factors suggested to in¯uence PC utilization or any of
the possible interaction factors.
PC utilization is in¯uenced strongly by degree of
PC Access (thus supporting H8), and experience (H9),
but not respondents' age (H10), education (H11)
and PC training (H7) and ownership of a PC. The
non-signi®cance of PC ownership is surprising but
understandable. Some respondents may own a PC
but may not use them. The PC may be acquired
for the use of their children. Some respondents who
do not own a PC may have one at home for of®cial
use. As a result, ownership of a PC is not a predictor
of PC utilization and is different from having access to
the PC.
7.2. The influence of attitude on PC utilization
Table 8 reports the intercorrelations among summarized group of attitude items, their mean values and
alphas. The results suggest that social factors, facilitating conditions, and Job Fit are signi®cantly correlated with PC utilization, while Affect is mildly
correlated with PC utilization.
Table 9 suggests that Social Factors, Affect, Job
Performance Facilitation and Facilitating Conditions
are signi®cant determinants of PC utilization. Complexity and Long-Term Consequences are not signi®-
200
M.A. Al-Khaldi, R.S.O. Wallace / Information & Management 36 (1999) 185±204
Table 8
Intercorrelations, means and alphas of summarized factors of computer utilization
Factors
1
1.
2.
3.
4.
5.
6.
7.
1.000
0.232 a 1.000
0.262 a 0.323 a 1.000
ÿ0.154 ÿ0.159 ÿ0.099
1.000
0.391 a 0.105
0.117 ÿ0.210 a 1.0000
0.234 a 0.477 a 0.385 a ÿ0.145 0.1780 1.0000
0.763 a 0.168 a 0.218 a ÿ0.116 0.2533 a 0.1113
a
Social
Affect
Job performance facilitation
Complexity
Facilitating conditions
Long-term consequences
Utilization
2
3
4
5
6
7
Mean
Standarddeviation
1.0000
4.17
4.05
4.32
2.13
3.76
4.03
4.20
0.62
0.70
0.67
0.83
0.88
0.68
0.72
0.70
0.44
0.68
0.